Contrastive Learning of Person-Independent Representations for Facial Action Unit Detection

doi:10.1109/TIP.2023.3279978

	Contrastive Learning of Person-Independent Representations for Facial Action Unit Detection
	Li, Yong 1; Shan, Shiguang 2,3,4
	2023
发表期刊	IEEE TRANSACTIONS ON IMAGE PROCESSING
ISSN	1057-7149
卷号	32 页码:3212-3225
摘要	Facial action unit (AU) detection, aiming to classify AU present in the facial image, has long suffered from insufficient AU annotations. In this paper, we aim to mitigate this data scarcity issue by learning AU representations from a large number of unlabelled facial videos in a contrastive learning paradigm. We formulate the self-supervised AU representation learning signals in two-fold: 1) AU representation should be frame-wisely discriminative within a short video clip; 2) Facial frames sampled from different identities but show analogous facial AUs should have consistent AU representations. As to achieve these goals, we propose to contrastively learn the AU representation within a video clip and devise a cross-identity reconstruction mechanism to learn the person-independent representations. Specially, we adopt a margin-based temporal contrastive learning paradigm to perceive the temporal AU coherence and evolution characteristics within a clip that consists of consecutive input facial frames. Moreover, the cross-identity reconstruction mechanism facilitates pushing the faces from different identities but show analogous AUs close in the latent embedding space. Experimental results on three public AU datasets demonstrate that the learned AU representation is discriminative for AU detection. Our method outperforms other contrastive learning methods and significantly closes the performance gap between the self-supervised and supervised AU detection approaches.
关键词	Gold Videos Training Image reconstruction Feature extraction Faces Task analysis Facial action unit detection contrastive Learning self-supervised learning person-independent action unit detection
DOI	10.1109/TIP.2023.3279978
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2018AAA0102402] ; National Natural Science Foundation of China[62102180] ; Natural Science Foundation of Jiangsu Province[BK20210329] ; Shuangchuang Program of Jiangsu Province[JSSCBS20210210]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:001004183400002
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/21214
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Shan, Shiguang
作者单位	1.Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Key Lab Intelligent Percept & Syst High Dimens Inf, Minist Educ, Nanjing 210094, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100049, Peoples R China 4.Peng Cheng Lab, Shenzhen 518055, Peoples R China
推荐引用方式 GB/T 7714	Li, Yong,Shan, Shiguang. Contrastive Learning of Person-Independent Representations for Facial Action Unit Detection[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2023,32:3212-3225.
APA	Li, Yong,&Shan, Shiguang.(2023).Contrastive Learning of Person-Independent Representations for Facial Action Unit Detection.IEEE TRANSACTIONS ON IMAGE PROCESSING,32,3212-3225.
MLA	Li, Yong,et al."Contrastive Learning of Person-Independent Representations for Facial Action Unit Detection".IEEE TRANSACTIONS ON IMAGE PROCESSING 32(2023):3212-3225.